1. Technical Field
The present invention relates to tracking the location of a mobile object, such as a container that is carried on ships, railroad cars or trucks, or stored in freight yards. The present invention provides an automatic means to correct positioning errors after real-time positions of the mobile object have been reported from a real-time positioning system.
2. Related Art
Position or location tracking is a crucial component of many inventory and resource monitoring and management systems. Typical location tracking systems employ real-time positioning sensors that continuously or periodically provide position solutions for location tracking. The “real-time” position solution indicates that such position data is computed or determined based on measurements only up to the time that this position data is computed or determined. Sensors or systems commonly acquire real-time locations of vehicles, equipment, or inventory based on principles of either triangulation or proximity with respect to known locations using various electronic positioning means such as a Global Positioning System (GPS), a Differential Global Positioning System (DGPS), a DGPS and Inertial Navigation System (INS) integrated system, a Real-time Locating System (RTLS), a RTLS/GPS integrated system, a RTLS/INS integrated system, transponders, an ultra wideband locating system, or some combinations of the above systems.
For example, U.S. Pat. No. 6,577,921 discloses a container tracking system that tracks the real-time positions of container handling equipments using GPS, INS and wireless communication. U.S. Pat. No. 6,657,586 describes an RTLS for locating objects, in which every object has a tag attached to it and every remote reader has a GPS receiver. U.S. Pat. No. 6,266,008 discloses a system and method for determining the location of freight containers in a freight yard by attaching GPS receivers to every container. U.S. Pat. No. 6,611,755 describes a timing control method for a fleet management system using a communication, status sensor and positioning system. U.S. Pat. No. 6,876,326 discloses a location tracking system using communication search mode ranging techniques.
Limitations in physics, however, generally prevent the real-time positioning systems from achieving 100% reliability or accuracy, especially in the constantly changing environment of heavy metal container yards. Examples of those limitations with respect to radio-wave positioning techniques such as GPS, DGPS, and RTLS include obstacles blocking line of sight position signals and signals reflected from nearby surfaces (creating multi-path errors). Further practical limitations in sensor technologies such as INS and other vehicle motion sensors include biases in measurements and poor signal-to-noise ratios due to environmental sources. These limitations result in common positioning errors such as inaccuracies, loss of position, or location drifts.
To overcome the physical and practical limitations, many real-time positioning systems employ complimentary sensors, or digital maps to improve accuracy and reliability. As an example, the complementary nature of INS and GPS is the main reason why GPS/INS or DGPS/INS integrated systems are popular. The high, long-term accuracy of GPS can be combined with the high output rate, robustness and reliability of INS to deliver superior positioning performance. The integration improves the real-time positioning performance statistically; that is, the integration typically reduces the variances of the position errors when the positioning sensors are subject to unfavorable operational conditions. However, no positioning system or method can guarantee a 100% real-time accuracy when the unfavorable conditions persist. In fact, a positioning error could be created and then propagate erroneous data into an inventory application.
In addition to INS, other sensors or techniques have been used to complement GPS for providing better measurements or estimations of the current positions. For example, U.S. Pat. Nos. 6,731,237, 6,697,736, 6,694,260, 6,516,272, 6,427,122, and 6,317,688 describe various techniques to integrate GPS systems with inertial sensors (e.g., gyros and accelerometers), altimeters, compass, or magnetometers to (statistically) improve either reliability or accuracy of real-time positioning. U.S. Pat. Nos. 6,766,247, 6,728,637, and 6,615,135 disclose specific methods to increase real-time positioning accuracy by incorporating map or route information in a GPS or other sensor. U.S. Pat. Nos. 6,826,478 and 6,615,136 disclose various techniques to increase the real-time GPS/INS positioning accuracy by incorporating additional sensor information or pre-stored map and location information. U.S. Pat. No. 6,853,687 describes a method to improve the real-time performance of the RTLS by incorporating magnetic field proximity-based pingers into the RFID tags.
These prior art methods focus on providing a real-time position solution that is the best estimate of the “current” position of a mobile object. Such a “real-time” position solution is resolved or computed based on measurements up to the time that this position data is referred to. To provide such a real-time position estimate, prior art methods use the real-time (i.e., the “current” or most recent) measurements from positioning sensors such as GPS and INS, the history information including previous measurements of the positioning sensors, and sometimes a pre-stored map or location information. Typically, model-based estimation (e.g., Kalman Filters) and other sensor fusion techniques are employed for the integration.
Errors or noise in the real-time position solutions are inevitable in practice. For example, even real-time position solutions from an expensive tightly-coupled DGPS/INS integrated system can drift away from the true positions when the mobile object on which the system is installed enters an “unfavorable” area with less than four GPS satellite coverage for a relatively long period of time. Thus, the real-time position estimates obtained when the mobile object is in the GPS-unfavorable area have a relatively low accuracy. When the mobile object comes out of such a GPS-unfavorable area, those previous (real-time) position estimates become past position estimates which are part of the history data. Meanwhile, the DGPS receiver begins to receive signals from more than four GPS satellites; therefore, the current real-time measurements become more accurate; as the prior art methods proceed to compute current real-time position estimates based on the new measurements and the history data (typically using only the last real-time position solution), the current real-time position estimates then have a higher accuracy as well. However, the past position estimates (i.e., the previous real-time position estimates) in the history data are not updated or modified based on the “current” higher-accuracy real-time position estimate. As a result, any error or noise in the previous (real-time) position solutions will remain.
In some applications, such as navigation systems and vehicle guidance systems, once a position estimate becomes obsolete, it is no longer important. For example, for a vehicle navigation system, advices such as turning left at the next intersection are based on the current position of the vehicle, not its position five seconds or minutes ago. If 4 seconds ago the vehicle was under an overpass and the GPS lost satellite signals, or even if the position estimate 4 seconds ago is several meters away from the true position, it no longer matters to the vehicle navigation system as long as the current position estimate is accurate. However, in other applications, such as a typical inventory or resource tracking environment, inaccurate position estimates, regardless current or previous, can propagate into widespread inventory or database errors if they are not corrected in time. This occurs especially when tracking the position of containers or vehicles in a warehouse, container yard, or rail yard where tracking signals can be blocked.
For example, a container handling equipment drops off Container A at time t(i); the positioning system provides a real-time position estimate P(i), which is then reported to the inventory management system. Accordingly, the inventory management system records that Container A is now at location P(i) in the inventory database. As time goes by, the positioning system continues to provide the real-time position estimates P(i+1), P(i+2), . . . for the subsequent time instances t(i+1), t(i+2), and so on, and the position estimate P(i) remains as the position estimate at time t(i). Therefore, if the “real-time” position estimate P(i) is inaccurate due to a relatively long period of GPS blockage, and the actual drop-off location is one container location away, this inaccurate position estimate P(i) would remain in the inventory database as the location of Container A. Since the prior art methods do not have the capability to correct position estimate P(i) after time instance t(i), the erroneous position estimate P(i) in the inventory database will not be corrected automatically. Instead, the resultant errors often require manual correction, creating delays in the operation.
If such position errors are not corrected in time, they can propagate and further corrupt the inventory database when the containers associated with the erroneous position estimates are moved without correction, resulting in often expensive corrective measures in resource management and inventory controls. Given the previous example, when the inventory management system sends a command for a pick-up operation of Container A, the command will specify to an operator that the location to pick up Container A is P(i); however, since P(i) is erroneous, the operator will not be able to find Container A at P(i). If another container, Container B, happens to be at location P(i), the operator may simply pick up Container B and proceed to the next command if he or she is not paying attention. As a result, the inventory management system will erroneously record all the subsequent operations that are actually related to Container B as operations on Container A. Similarly, the operations on Container A can be mistaken as the operation on another container. Thus, one single position error can propagate and corrupt the inventory database to a significant extent.